Improved Guided A-Star Path Planning Method Based on APF Algorithm

DOI

10.2991/978-94-6463-821-9_108How to use a DOI?

Keywords

Path planning; Dynamic obstacle avoidance; Artificial potential field method

Abstract

Path planning is a core challenge in robot autonomous navigation. Traditional artificial potential field methods have problems such as local minimum traps, while the A* algorithm, although capable of generating globally highest-quality trajectorys, cannot handle dynamic environments. The separation of these methods restricts the application efficiency of robots in real-time scenarios. This research introduces an enhanced APF algorithm, guided by A* macro-level navigation planning. By using the node sequence planned by A* as the anchor points for the potential field direction and reconstructing the repulsive field boundary range, this attempt aims to address the local minima issue inherent in traditional APF algorithms and improve their dynamic obstacle avoidance capabilities. The result shows this integrated framework enables robots to inherit the reliable macro-level navigation planning capability of the A* algorithm while leveraging the real-time obstacle avoidance agility of APF. The improved A* algorithm optimizes path length by 5.4% and reduces turns by 33.3% on average. Meanwhile, the improved APF algorithm shows significant advantages in dynamic obstacle avoidance. It has faster response and larger safe distance than traditional APF. For example, in extreme speed combination (0.125:1.25), traditional APF fails while improved APF responds in 0.125 s with 2.313 m safe distance.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Junjie Yang
PY  - 2025
DA  - 2025/08/31
TI  - Improved Guided A-Star Path Planning Method Based on APF Algorithm
BT  - Proceedings of the 2025 2nd International Conference on Mechanics, Electronics Engineering and Automation (ICMEEA 2025)
PB  - Atlantis Press
SP  - 1131
EP  - 1144
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-821-9_108
DO  - 10.2991/978-94-6463-821-9_108
ID  - Yang2025
ER  -